Disease-gene relations extraction using domain dictionaries and named entity recognition filtering

• Item type: SIG Technical Reports(1)
• 公開日: 2005-12-22
• タイトル: Disease-gene relations extraction using domain dictionaries and named entity recognition filtering
• タイトル / 言語: en
• タイトル: Disease-gene relations extraction using domain dictionaries and named entity recognition filtering
• 言語: eng
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: University of Tokyo
• 著者所属: University of Tokyo CREST Japan Science and Technology agency
• 著者所属: University of Tokyo CREST Japan Science and Technology agency School of Informatics University of Manchester
• 著者所属(英): en
• 著者所属(英): University of Tokyo
• 著者所属(英): en
• 著者所属(英): University of Tokyo,CREST Japan Science and Technology agency
• 著者所属(英): en
• 著者所属(英): University of Tokyo,CREST Japan Science and Technology agency,School of Informatics University of Manchester
• 著者名: Hong-WooChun; Yoshimasa, Tsuruoka; Jun'ichi, Tsujii
• 著者名(英): Hong-Woo, Chun; Yoshimasa, Tsuruoka; Jun'ichi, Tsujii
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: We extracted disease-gene relations from MedLine using disease/gene dictionaries which are constructed from six public DBs. Since dictionary matching produces a large number of false positives we developed a method of machine learning-based named entity recognition (NER) to filter out false recognitions of disease/gene names. We found that the performance of relation extraction depends on the performance of NER filtering and that the filtering improves the precision of relation extraction by 26.7% at the cost of a small reduction in recall.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: We extracted disease-gene relations from MedLine using disease/gene dictionaries which are constructed from six public DBs. Since dictionary matching produces a large number of false positives, we developed a method of machine learning-based named entity recognition (NER) to filter out false recognitions of disease/gene names. We found that the performance of relation extraction depends on the performance of NER filtering and that the filtering improves the precision of relation extraction by 26.7% at the cost of a small reduction in recall.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA12055912
• 書誌情報: 情報処理学会研究報告バイオ情報学（BIO）; 巻 2005, 号 128(2005-BIO-003), p. 81-87, 発行日 2005-12-22
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会